Predictions are what generate the value. The fun part!
You can use Aito to predict "things" via different means, which follow the same paths as the data uploads.
- Use Aito HTTP API to make requests to query endpoints, and read predictions from responses (JSON). Note that this is the most common option when using Aito with most of the automation platforms, such as Zapier, Integromat/Make and UiPath.
- Use Aito Python SDK's predict functions to send queries to Aito, and read the results from the response JSON. This would be the recommended method when working with a Python native platform, such as Robocorp.
- Use the Airtable Extension to make get predictions automatically without code, based on your interactions with the Airtable base. This is the recommended method for users not having a software development background.
Regardless of the chosen method, the big picture is simple. Once you have a dataset in Aito, you can choose any of the available predictive functions and give needed inputs, and read the results from Aito's response. As a usage paradigm, this is closer to interacting with a database than a machine learning model.
An example of an Aito
_predictquery (request) would be for example:
The response to the said query would be as below, indicating only ~11% survival prediction for Titanic's male passengers with a third-class ticket. 😥
First of all, at this point, only categorical predictions are supported. We are planning to add support for other types of predictions (such as forecasting) later on.
The main types of predictions/queries are reflected in the Aito API as endpoints:
An easy way to check that all things are working great is to test the predictions in Aito Console first. Once the data is uploaded, navigate to the Predictions tab. Check out this quick video, or ream more below.
There you can choose any table and prediction target column from the dropdown. Aito then populates the request with one sample row from the data to give you a quick starting point. You may edit the input data, and remove unnecessary inputs. The right side shows your JSON request body in real-time.
Once the query is good, hit Predict and Aito returns the results.
The resulting view has both a visual representation of the probabilities, as well as the raw output that came with Aito's response.